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Cuccurullo V, Rapa M, Catalfamo B, Gatta G, Di Grezia G, Cascini GL. The Role of Imaging of Lymphatic System to Prevent Cancer Related Lymphedema. Bioengineering (Basel) 2023; 10:1407. [PMID: 38135998 PMCID: PMC10740912 DOI: 10.3390/bioengineering10121407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/02/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Lymphedema is a progressive chronic condition affecting approximately 250 million people worldwide, a number that is currently underestimated. In Western countries, the most common form of lymphedema of the extremities is cancer-related and less radical surgical intervention is the main option to prevent it. Standardized protocols in the areas of diagnosis, staging and treatment are strongly required to address this issue. The aim of this study is to review the main diagnostic methods, comparing new emerging procedures to lymphoscintigraphy, considered as the golden standard to date. The roles of Magnetic Resonance Lymphangiography (MRL) or indocyanine green ICG lymphography are particularly reviewed in order to evaluate diagnostic accuracy, potential associations with lymphoscintigraphy, and future directions guided by AI protocols. The use of imaging to treat lymphedema has benefited from new techniques in the area of lymphatic vessels anatomy; these perspectives have become of value in many clinical scenarios to prevent cancer-related lymphedema.
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Affiliation(s)
- Vincenzo Cuccurullo
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (M.R.); (G.G.)
| | - Marco Rapa
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (M.R.); (G.G.)
| | - Barbara Catalfamo
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; (B.C.); (G.L.C.)
| | - Gianluca Gatta
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (M.R.); (G.G.)
| | | | - Giuseppe Lucio Cascini
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; (B.C.); (G.L.C.)
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Cuccurullo V, Rapa M, Catalfamo B, Cascini GL. Role of Nuclear Sentinel Lymph Node Mapping Compared to New Alternative Imaging Methods. J Pers Med 2023; 13:1219. [PMID: 37623469 PMCID: PMC10455335 DOI: 10.3390/jpm13081219] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/22/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
With the emergence of sentinel node technology, many patients can be staged histopathologically using lymphatic mapping and selective lymphadenectomy. Structural imaging by using US, CT and MR permits precise measurement of lymph node volume, which is strongly associated with neoplastic involvement. Sentinel lymph node detection has been an ideal field of application for nuclear medicine because anatomical data fails to represent the close connections between the lymphatic system and regional lymph nodes, or, more specifically, to identify the first draining lymph node. Hybrid imaging has demonstrated higher accuracy than standard imaging in SLN visualization on images, but it did not change in terms of surgical detection. New alternatives without ionizing radiations are emerging now from "non-radiological" fields, such as ophthalmology and dermatology, where fluorescence or opto-acoustic imaging, for example, are widely used. In this paper, we will analyze the advantages and limits of the main innovative methods in sentinel lymph node detection, including innovations in lymphoscintigraphy techniques that persist as the gold standard to date.
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Affiliation(s)
- Vincenzo Cuccurullo
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80138 Napoli, Italy
| | - Marco Rapa
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80138 Napoli, Italy
| | - Barbara Catalfamo
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy (G.L.C.)
| | - Giuseppe Lucio Cascini
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy (G.L.C.)
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Lopci E, Elia C, Catalfamo B, Burnelli R, De Re V, Mussolin L, Piccardo A, Cistaro A, Borsatti E, Zucchetta P, Bianchi M, Buffardi S, Farruggia P, Garaventa A, Sala A, Vinti L, Mauz-Koerholz C, Mascarin M. Prospective Evaluation of Different Methods for Volumetric Analysis on [ 18F]FDG PET/CT in Pediatric Hodgkin Lymphoma. J Clin Med 2022; 11:jcm11206223. [PMID: 36294544 PMCID: PMC9605658 DOI: 10.3390/jcm11206223] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/27/2022] [Accepted: 10/17/2022] [Indexed: 11/30/2022] Open
Abstract
Rationale: Therapy response evaluation by 18F-fluorodeoxyglucose PET/CT (FDG PET) has become a powerful tool for the discrimination of responders from non-responders in pediatric Hodgkin lymphoma (HL). Recently, volumetric analyses have been regarded as a valuable tool for disease prognostication and biological characterization in cancer. Given the multitude of methods available for volumetric analysis in HL, the AIEOP Hodgkin Lymphoma Study Group has designed a prospective analysis of the Italian cohort enrolled in the EuroNet-PHL-C2 trial. Methods: Primarily, the study aimed to compare the different segmentation techniques used for volumetric assessment in HL patients at baseline (PET1) and during therapy: early (PET2) and late assessment (PET3). Overall, 50 patients and 150 scans were investigated for the current analysis. A dedicated software was used to semi-automatically delineate contours of the lesions by using different threshold methods. More specifically, four methods were applied: (1) fixed 41% threshold of the maximum standardized uptake value (SUVmax) within the respective lymphoma site (V41%), (2) fixed absolute SUV threshold of 2.5 (V2.5); (3) SUVmax(lesion)/SUVmean liver >1.5 (Vliver); (4) adaptive method (AM). All parameters obtained from the different methods were analyzed with respect to response. Results: Among the different methods investigated, the strongest correlation was observed between AM and Vliver (rho > 0.9; p < 0.001 for SUVmean, MTV and TLG at all scan timing), along with V2.5 and AM or Vliver (rho 0.98, p < 0.001 for TLG at baseline; rho > 0.9; p < 0.001 for SUVmean, MTV and TLG at PET2 and PET3, respectively). To determine the best segmentation method, we applied logistic regression and correlated different results with Deauville scores at late evaluation. Logistic regression demonstrated that MTV (metabolic tumor volume) and TLG (total lesion glycolysis) computation according to V2.5 and Vliver significantly correlated to response to treatment (p = 0.01 and 0.04 for MTV and 0.03 and 0.04 for TLG, respectively). SUVmean also resulted in significant correlation as absolute value or variation. Conclusions: The best correlation for volumetric analysis was documented for AM and Vliver, followed by V2.5. The volumetric analyses obtained from V2.5 and Vliver significantly correlated to response to therapy, proving to be preferred thresholds in our pediatric HL cohort.
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Affiliation(s)
- Egesta Lopci
- Nuclear Medicine Unit, IRCCS—Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy
- Correspondence: or
| | - Caterina Elia
- AYA and Pediatric Radiotherapy Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Barbara Catalfamo
- Nuclear Medicine Unit, University Hospital “Mater Domini, 88100 Catanzaro, Italy
| | - Roberta Burnelli
- Pediatric Onco-Hematologic Unit, University Hospital S. Anna, 44121 Ferrara, Italy
| | - Valli De Re
- Immunopathology and Cancer Biomarkers Unit, Department of Translational Research, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Lara Mussolin
- Pediatric Hemato-Oncology Clinic, Department of Women’s and Children’s Health, University of Padua, 35128 Padua, Italy
- Institute of Pediatric Research-Fondazione Città della Speranza, 35127 Padua, Italy
| | - Arnoldo Piccardo
- Department of Nuclear Medicine, Galliera Hospital, 16128 Genoa, Italy
| | - Angelina Cistaro
- Nuclear Medicine Division, Salus Alliance Medical, 16128 Genoa, Italy
| | - Eugenio Borsatti
- Nuclear Medicine Department, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Pietro Zucchetta
- Nuclear Medicine Department, Padova University Hospital, 35128 Padua, Italy
| | - Maurizio Bianchi
- Onco-Hematology Division, Regina Margherita Hospital, 10126 Torino, Italy
| | - Salvatore Buffardi
- Department of Oncology, Hospital Santobono-Pausilipon, 80123 Naples, Italy
| | - Piero Farruggia
- Department of Pediatric Onco-Hematology, A.R.N.A.S. Ospedali Civico, 90127 Palermo, Italy
| | - Alberto Garaventa
- Pediatric Oncology Unit, I RCCS G.Gaslini Hospital, 16147 Genoa, Italy
| | - Alessandra Sala
- Pediatric Division, Hospital San Gerardo, 20900 Monza, Italy
| | - Luciana Vinti
- Department of Pediatric Hematology and Oncology, Ospedale Bambino Gesù, IRCSS, 00165 Rome, Italy
| | - Christine Mauz-Koerholz
- Pädiatrische Hämatologie und Onkologie, Zentrum für Kinderheilkunde der Justus-Liebig-Universität Gießen, 35392 Giessen, Germany
- Medizinische Fakultät der Martin-Luther-Universität Halle-Wittenberg, 06120 Halle, Germany
| | - Maurizio Mascarin
- AYA and Pediatric Radiotherapy Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
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Flannery C, Buddin K, Begum L, Nasert M, Catalfamo B, Semler E, Fortier L. QUANTITATIVE COMPOSITIONAL AND BIOACTIVITY ANALYSES OF A NOVEL PLACENTAL TISSUE BIOLOGIC (PTP-001) COMPARED WITH PLATELET RICH PLASMA (PRP). Osteoarthritis Cartilage 2022. [DOI: 10.1016/j.joca.2022.02.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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Mapelli P, Bezzi C, Palumbo D, Canevari C, Ghezzo S, Samanes Gajate AM, Catalfamo B, Messina A, Presotto L, Guarnaccia A, Bettinardi V, Muffatti F, Andreasi V, Schiavo Lena M, Gianolli L, Partelli S, Falconi M, Scifo P, De Cobelli F, Picchio M. 68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours. Eur J Nucl Med Mol Imaging 2022; 49:2352-2363. [DOI: 10.1007/s00259-022-05677-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/31/2021] [Indexed: 12/17/2022]
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Russo G, Stefano A, Alongi P, Comelli A, Catalfamo B, Mantarro C, Longo C, Altieri R, Certo F, Cosentino S, Sabini MG, Richiusa S, Barbagallo GMV, Ippolito M. Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model. Curr Oncol 2021; 28:5318-5331. [PMID: 34940083 PMCID: PMC8700249 DOI: 10.3390/curroncol28060444] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 12/12/2022] Open
Abstract
Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.
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Affiliation(s)
- Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), 90015 Cefalù, Italy; (G.R.); (A.S.); (A.C.); (S.R.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), 90015 Cefalù, Italy; (G.R.); (A.S.); (A.C.); (S.R.)
| | - Pierpaolo Alongi
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, 90015 Cefalù, Italy; (B.C.); (C.M.); (C.L.)
- Correspondence:
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), 90015 Cefalù, Italy; (G.R.); (A.S.); (A.C.); (S.R.)
- Ri.MED Foundation, 90133 Palermo, Italy
| | - Barbara Catalfamo
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, 90015 Cefalù, Italy; (B.C.); (C.M.); (C.L.)
- Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, Nuclear Medicine Unit, University of Messina, 98168 Messina, Italy
| | - Cristina Mantarro
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, 90015 Cefalù, Italy; (B.C.); (C.M.); (C.L.)
- Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, Nuclear Medicine Unit, University of Messina, 98168 Messina, Italy
| | - Costanza Longo
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, 90015 Cefalù, Italy; (B.C.); (C.M.); (C.L.)
- Ri.MED Foundation, 90133 Palermo, Italy
| | - Roberto Altieri
- Neurosurgical Unit, AOU Policlinico “G. Rodolico-San Marco”, University of Catania, 95123 Catania, Italy; (R.A.); (F.C.); (G.M.V.B.)
- Interdisciplinary Research Center on Diagnosis and Management of Brain Tumors, University of Catania, 95123 Catania, Italy
| | - Francesco Certo
- Neurosurgical Unit, AOU Policlinico “G. Rodolico-San Marco”, University of Catania, 95123 Catania, Italy; (R.A.); (F.C.); (G.M.V.B.)
- Interdisciplinary Research Center on Diagnosis and Management of Brain Tumors, University of Catania, 95123 Catania, Italy
| | - Sebastiano Cosentino
- Nuclear Medicine Department, Cannizzaro Hospital, 95123 Catania, Italy; (S.C.); (M.G.S.); (M.I.)
| | - Maria Gabriella Sabini
- Nuclear Medicine Department, Cannizzaro Hospital, 95123 Catania, Italy; (S.C.); (M.G.S.); (M.I.)
| | - Selene Richiusa
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), 90015 Cefalù, Italy; (G.R.); (A.S.); (A.C.); (S.R.)
| | - Giuseppe Maria Vincenzo Barbagallo
- Neurosurgical Unit, AOU Policlinico “G. Rodolico-San Marco”, University of Catania, 95123 Catania, Italy; (R.A.); (F.C.); (G.M.V.B.)
- Interdisciplinary Research Center on Diagnosis and Management of Brain Tumors, University of Catania, 95123 Catania, Italy
| | - Massimo Ippolito
- Nuclear Medicine Department, Cannizzaro Hospital, 95123 Catania, Italy; (S.C.); (M.G.S.); (M.I.)
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